Functional Ecology 2009, 23, 17–26
doi: 10.1111/j.1365-2435.2008.01529.x
NUTRITIONAL ECOLOGY
Blackwell Publishing Ltd
Ten years of experimental animal isotopic ecology
SIA in animal ecology
Nathan Wolf*, Scott A. Carleton and Carlos Martínez del Rio
Department of Zoology and Physiology, University of Wyoming, Laramie, WY 82071-3166, USA
Summary
1. Ten years ago Gannes et al. (1997, Stable isotopes in animal ecology: assumptions, caveats, and
a call for laboratory experiments. Ecology, 78, 1271–1276, 1998) identified four major areas requiring
further research in experimental animal isotopic ecology: (i) the dynamics of isotopic incorporation,
(ii) mixing models, (iii) the problem of routing, and (iv) trophic discrimination factors.
2. Differences in isotopic incorporation rates among tissues seem to be explained by variation in
protein turnover. The application of multi-compartment models to isotopic incorporation data has
revealed that different inferences can be derived between these and one-compartment models.
3. A variety of mixing models of varying degrees of complexity and realism are used to find the
contribution of isotopic sources to the elements in an organism’s tissues. The use of these models
demands the use of tissue to diet discrimination factors that are rarely measured experimentally.
4. Mixing models assume that assimilated nutrients are disassembled into their elemental components and that these elements are reassembled into biomolecules. This assumption is unrealistic
as macromolecules are routed differentially into tissues. Isotopic routing is an area that isotopic
ecologists have neglected in their experimental and modelling research.
5. Isotopic ecologists are just beginning to understand why 15N biomagnifies along trophic chains,
and to explore the factors that determine the degree of 15N biomagnification. We review the hypotheses
that explain why 15N biomagnifies up trophic chains.
6. The use of compound-specific isotopic analyses is opening new fruitful areas of research at the
intersection of nutritional and isotopic ecology.
Key-words: δ13C, δ15N, experimental isotopic ecology, stable isotopes, trophic ecology
Introduction
Over the last 10–15 years, animal ecologists have embraced
stable isotope analysis (SIA). During this period, the applications of SIA to the study of animals has grown rapidly and
ecologists have applied SIA to all areas of animal ecology
ranging from paleoecology to ecosystem ecology, passing
through physiological and population ecology (Hobson &
Wassenaar 1999; Martínez del Rio & Wolf 2005; Koch 2007).
The successful adoption of SIA by animal ecologists is the
result of (i) technological progress, (ii) large observational
data sets, (iii) experimental research, and (iv) the development
of theoretical models. 10 years ago, Gannes et al. (1997, 1998)
predicted that SIA would grow rapidly and called for laboratory
experiments (Gannes et al. 1997). Here we will review the
experimental studies that have taken place 10 years after
Gannes et al.’s (1997) call for more experimentation. Our
primary focus will not be the many insights that ecologists
have gained using stable isotopes. Instead, we will focus on
(i) areas in which further experimentation is still needed,
*Correspondence author. E-mail: nwolf@uwyo.edu
(ii) describe models that ecologists use to interpret experimental
results, and (iii) identify areas in which theoretical research
and development are still needed.
Gannes et al. (1997, 1998) proposed that the successful
application of SIA to animal ecology hinged on our knowledge
of how rapidly and faithfully animals incorporate the isotopic
composition of their food. They identified four areas in which
experimental work was needed: (i) the dynamics of isotopic
incorporation, (ii) mixing models, (iii) the problem of routing,
and (iv) trophic discrimination factors. These areas are the
organizing foci of this review. In a final section we will identify
novel research themes that we believe are ripe for exploration.
We begin our review by justifying why a review on the use of
stable isotopes has a place in an issue of Functional Ecology
devoted to nutritional ecology.
ISOTOPIC ECOLOGY AS AN APPLICATION OF
NUTRITIONAL ECOLOGY
Nutritional ecology investigates the evolutionary causes and
ecological consequences of how animals acquire and process
resources (Karasov & Martinez del Rio 2007). George
© 2009 The Authors. Journal compilation © 2009 British Ecological Society
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N. Wolf et al.
Bartholomew (1964) wrote that ‘Every level of biological
organization finds its mechanism at lower levels of biological
organization and its significance at higher levels of biological organization’. We believe that isotopic ecology receives
its meaning from its relevance in population, community, and
ecosystem ecology, and has its mechanistic foundation
in nutritional ecology. We will attempt to establish clear
connections between patterns that ecologists use and their
known or possible physiological causes.
Dynamics of isotopic incorporation
IMPORTANCE TO ECOLOGISTS
Isotopic ecologists should be interested in the time-scale of
the incorporation of the isotopic signature into an animal’s
tissues because this information determines the time window
through which they can perceive the course of diet changes in
an animal (Dalerum & Angerbjörn 2005; Newsome et al. 2007).
By sampling different types of tissues in a single individual,
SIA allows exploration of how animals use resources over a
variety of temporal scales (reviewed by Phillips & Eldridge
2006). In vertebrates, some tissues, such as liver and plasma
proteins have high rates of isotopic incorporation, and their
isotopic composition reflects integration of recent dietary
inputs. Others, such as bone collagen, exhibit low incorporation
rates, and their isotopic composition reflects integration
over longer time periods (Dalerum & Angerbjörn 2005 and
references therein). Some tissues (e.g. feathers, hair, and shells)
are deposited in discrete intervals, remain inert, and retain
the isotopic composition of resources incorporated while
they were manufactured (Bowen et al. 2005).
ONE COMPARTMENT, FIRST-ORDER MODELS
The time course of isotopic incorporation is determined
experimentally. A group of animals whose tissues have reached
equilibrium with one diet are shifted to another diet with a
different isotopic composition (Martínez del Rio & AndersonSprecher 2008). The relationship between the composition of
animal’s tissues (δX(t)tissue, where X is an isotope) and time
(t) has been traditionally described by exponential functions
of the form δXtissue(t) = a + be−ct, where a, b, and c are estimated
empirically (Bearhop et al., 2002 and references therein).
This equation can be written, perhaps more intuitively, as
δXtissue(t) = δX∞ − (δX∞ − δXtissue(0))e−λt,
eqn 1
where a = δX ∞, b = −( δX∞ − δX tissue(0)), and c = λ. Eqn 1
represents the behaviour of a well-mixed, one-compartment
system with first order kinetics (Martínez del Rio & Wolf
2005; Olive et al. 2003). The average residence time of an element
in systems described by eqn 1 equals 1/λ and the median
residence time (or half-life, t1/2) equals Ln (2)/λ. (Μartínez del
Rio & Anderson-Sprecher 2008). Although we contend that
using eqn 1 in all cases is incorrect (see Cerling et al. 2007a),
its application has been profitable.
WHY THERE ARE DIFFERENCES IN ISOTOPIC
INCORPORATION AMONG ANIMALS?
The rate at which animals incorporate the isotopic signal of
their food differs among organisms and tissues. The factors
that have been recognized (or hypothesized) to influence
incorporation rate are catabolism (protein turnover), growth
and body mass (mb). Carleton & Martínez del Rio (2005)
predicted that λ should be proportional to mb−1/4, and a data
set on the rate of 13C incorporation into the red blood cells of
several bird species verified their prediction. This result
suggests that isotopic ecologists may not use incorporation
data of an animal to infer the incorporation rate of another of
a different size. However, an animal’s body size is not the only
determinant of the rate at which its tissues incorporate the
isotopic composition of diet. The value of λ is determined by
both growth and by catabolic turnover (Fry & Arnold 1982).
Hesslein et al. (1993) proposed that the value of λ equals
the sum of fractional net growth kg (kg = mb−1[dmb /dt]) and
catabolic turnover kd (λ = kg + kd).
If isotopic incorporation can be described adequately by
eqn 1, we can summarize the effects of growth and catabolism
on λ as follows:
λ=
1 ⎛ dmb ⎞
θ
⎜
⎟ + αmb
mb ⎝ dt ⎠
eqn 2
This equation states that the fractional rate of isotopic
incorporation equals the sum of fractional growth rate and
the allometric effect of body size on catabolic turnover. The
parameters α and θ are empirically derived constants. We
speculate that the value of θ approximately equals −0·25
(Carleton & Martínez del Rio 2005). Because temperature has
a profound effect on all metabolic processes (Gillooly et al.
2001), we expect the magnitude of the allometric term to depend
on temperature (Witting et al. 2004) and differ between
endotherms and ectotherms. Although the predictions embodied
in eqn 2 have not been tested quantitatively, available data
is consistent with them. Isotopic incorporation is rapid in fast
growing ectotherms. (Jardine et al. 2004; Suzuki et al. 2005
and references therein, McIntyre & Flecker 2006; Reich et al.
2008), and the contribution of growth to λ in the tissues of
ectotherms is high (from 30% to 100%). In contrast, MacAvoy
et al. (2005) found that growth accounted for only c. 10% of the
rate of incorporation of carbon and nitrogen in adult mice.
Tieszen et al. (1983) warned about the ‘... important complication ... that each tissue ... can be expected to have an
isotopic memory’. The combined effect of body size and
growth on incorporation rate may exacerbate this complication
in large animals, such as ungulates and seals, and in the ‘slow’
tissues that are often used to study them (bone collagen, Koch
2007). In large animals the diet ingested during growth may
give collagen an imprint that lasts for a long time after growth
has ceased. Thus, the contribution of diets ingested after
animals are fully grown may be difficult to detect. The confounding effects of growth on stable isotope analyses are
probably a prevalent, and relatively unstudied, confounding
factor in stable isotope field studies (Reich et al. 2008).
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 17–26
SIA in animal ecology 19
DIFFERENCES IN ISOTOPIC INCORPORATION AMONG
TISSUES
Tieszen et al. (1983) speculated that ‘more metabolically
active tissues ... have faster turnover than less metabolically
active tissues’. They supported this hypothesis with a negative
correlation between oxygen consumption data measured
in vitro and the half-life of 13C. Tieszen et al.’s (1983) statement
has come to be interpreted to mean that both organisms and
tissues with high metabolic rate, construed narrowly as a high
rate of oxygen consumption, should have fast rates of isotopic
incorporation (Hobson & Clark 1992; Voigt et al. 2003).
Experimental evidence does not support this widely held
assumption (Voigt et al. 2003; Carleton & Martínez del Rio
2005). Why do we find these discrepancies between experimental observations and a reasonable hypothesis?
In their original article, Tieszen et al. (1983) melded two
concepts: metabolic activity interpreted as the collection of
anabolic and catabolic processes, and metabolic rate construed as the rate of oxygen consumption. MacAvoy et al.’s
(2006) study exacerbated this conflation when they reported a
negative correlation between mass-specific basal metabolic
rate (MR/mb) and the half-life of isotopic incorporation. The
half-life of a tissue-forming element (t1/2) scales with body
mass to the 0·25 power (Carleton & Martínez del Rio 2005)
and mass specific metabolic rate (MR/mb) scales with mb−1/4
(West et al. 1997). Therefore t1/2 must scale with (MR/mb)–1.
MacAvoy et al.’s (2006) negative correlation is a consequence
of the allometric dependence of both t1/2 and MR/mb on body
mass. Although oxygen consumption is related to metabolism
in the broad sense, the relationship is not direct and respiration
rate can be uncoupled from some components of secondary
metabolism (Marsh et al. 2001).
Carleton & Martínez del Rio (2005) speculated that the
primary determinant of the rate of isotopic incorporation
in most tissues (whose isotopic composition is typically
measured after lipids are extracted, Post 2007) is protein
turnover (Lobley, 2003). This conjecture leads to two predictions: (i) the rate of isotopic incorporation into different
organs/tissues should be ranked in the same order as their rate
of protein turnover, and (ii) because physiologists have
documented increases in protein synthesis resulting from
increased protein intake in a variety of vertebrates (see Lobley
2003; Tsahar et al. 2007 and reviewed by Waterlow 2006),
protein intake should influence isotopic incorporation rates.
Testing Carleton & Martínez del Rio’s (2005) conjecture
requires measuring protein turnover and isotopic incorporation
concurrently.
Although these measurements have not been done, there is
experimental support for the hypotheses’ two predictions.
Splanchnic organs with high rates of protein turnover such as
the liver and intestine have higher rates of isotopic incorporation
than collagen and muscle (Dalerum & Angerbjörn 2005, Fig. 1),
and Tsahar et al. (2007) documented a 36–60% increase in the
retention time of 15N in blood cells and plasma when they
reduced the nitrogen content of the diet of a fruit-eating bird
(Pycnonotus xanthopygos). Voigt et al. (2003) and Mirón
Fig. 1. The time course of incorporation of the isotopic composition
of a diet into an animal’s tissues can be described by either one- or
two-compartment models. In house sparrows, Passer domesticus,
whether a one- or a two-compartment model is best supported by
data depends on tissue. Open bars in A represent tissues in which a
one-compartment model is better supported by data, whereas closed
bars represent those tissues in which data supported a twocompartment model. Panel B compares the fit of a one- and a twocompartment model to the incorporation of 13C into intestinal tissue
of house sparrows after a change in diet. Note that the difference
between the asymptotic value of δ13C and diet (i.e. Δ13Ctissue-diet) is
smaller for the two compartment model.
et al. (2006) reported contrasting rates of isotopic incorporation into the tissues of the same species of nectar feeding
bat. These differences were explained by differences in protein
intake (Mirón et al. 2006). Several studies have demonstrated
increases in protein turnover with increased activity (Pikosky
et al. 2006 and references therein). The link between protein
turnover and isotopic incorporation rates suggests the
intriguing possibility of the effect of activity on the rate of
isotopic incorporation. To our knowledge, this possibility has
not been yet tested.
SHOULD WE USE MULTICOMPARTMENT MODELS?
Physiologists studying protein turnover typically have relied
on multi-compartment models (Waterlow 2006). Ayliffe
et al. (2004), and Cerling et al. (2007a,b) have argued that by
using-one compartment models, isotopic ecologists have
over-simplified a complex process. Cerling et al. (2007a)
proposed a graphical method they called the ‘reaction
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 17–26
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N. Wolf et al.
progress variable’ to diagnose whether isotopic incorporation
data must be described by one- or multi-compartment models.
Martínez del Rio & Anderson-Sprecher (2008) extended this
method and proposed statistical estimates of average retention
time for isotopes in multi-compartment systems and of the
uncertainty associated with these estimates. They also
proposed the use of model comparison approaches to assess
the weight of evidence in favour of one- or multi-compartment
models. We do not know yet if the application of these models
will force us to reconsider the inferences hard won with studies
that used one-compartment models. Because these patterns
are strong, we suspect that they will be robust to model
structure. However, details might change. For example,
Carleton et al. (2008) found that using two compartment
models consistently estimated a higher average retention time
for carbon (Fig. 1) and a discrimination factor with a lower
absolute magnitude.
Mixing models
MIXING MODELS
Mixing models are the tool of choice to estimate the contribution
of different sources to the tissues of an animal. In a mixing model we
attempt to estimate the fractional contribution of an isotopic source
to a tissue from the isotopic composition of the tissue and from the
isotopic composition of the dietary sources. The simplest mixing
model is of the form δT = fAδA + (1−fA)δB, where δ T is the isotopic
composition of an animal’s tissue, δA and δB are the isotopic compositions of sources A and B, and fA and (1 − fA) are their relative
fractional contributions (Phillips 2001). This model can be generalized
to a linear system of N equations in N unknowns that allows
estimating the contribution of N sources if one measures the composition of N – 1 isotopes in a tissue. Most studies rely on two isotopes
(13C and 15N), and thus:
δ13CT = fAδ13CA + fBδ13CB + fCδ13CC
δ13CT = fAδ15NA + fBδ15NB + fCδ15NC
1 = fA + fB + fC
eqn 3
We emphasize that fi estimates the contribution of source i to the
isotopic composition of a tissue. It does not estimate the fraction of
source i in the animal’s diet (see Martínez del Rio & Wolf 2005).
Using eqn 3 to estimate contributions of different sources to diet
assumes (i) that the elemental composition (i.e. the C : N ratio) of all
the diets is equal, (ii) that the efficiency with which each element in
each source is assimilated is the same, (iii) that there is no tissue to
diet discrimination, and (iv) that there is no isotopic routing. The
variation among sources in elemental ratios and in assimilation
efficiency can be addressed relatively easily with concentrationdependent mixing models (Phillips & Koch 2002) and by adding an
assimilation efficiency term to the models (Martínez del Rio & Wolf
2005). Available computer programs to estimate isotopic sources
(Isosource, SIAR and SISUS, which you can easily find using
Google) can address these complications. In eqn 3 the number of
unknowns and equations is the same, and therefore one can easily
find an analytical solution. Ecologists may face situations in which
the number of sources (N) is higher than the minimal number of
sources needed to constrain the system to a single solution (Phillips
& Gregg 2003). In such a case, the number of possible solutions is
infinite. Available computer programs can estimate the combinations of source proportions that satisfy eqn 3 and therefore provide
researchers with a space of feasible solutions.
TISSUE TO DIET DISCRIMINATION FACTORS
The term ‘tissue to diet discrimination’ (denoted by Δ) refers to the
difference in isotopic composition between a tissue and diet (i.e.
Δ = δtissue−δdiet). If discrimination factors are measured experimentally,
we can include them in a mixing model, δT = fA(δA + ΔA) + (1 −
fA)(δB + ΔB). Discrimination factors vary among species, among
tissues within a single species, and among diets (e.g. McCutchan
et al. 2003), and are not often measured experimentally in field
studies. Sometimes researchers used the average Δ value reported in
large reviews. Because 3·4‰ is the average Δ15Ntissue−diet value reported
in several reviews (Post 2002 and references therein), this number is
frequently used as a discrimination factor, but other values are used
as well. Some researchers use Δ values from related species fed on
similar diets, but others used values from unrelated species fed on
different diets (reviewed by Caut et al. 2008).
How big an error do researchers make when they use the wrong
discrimination factor? Assuming that ΔA and ΔB are equal (ΔA = ΔB
= Δ), the difference between the estimated value ( fA*( Δ*)) and the
real value ( fA) is given by:
fA*( Δ*) − fA =
Δ − Δ*
,
δ A − δB
eqn 4
where Δ* is the guessed discrimination factor. Errors in the estimation
of the fractional contribution of a source are smaller when the isotopic
difference between the sources is large. Many studies rely on two isotopes to estimate the proportional contribution of three sources (see
eqn 3). In such cases there are six possible unknown Δ values for three
diets, greatly increasing the potential errors that result from using
erroneous discrimination factors. Caut et al. (2008) found that the
models worked best when they used discrimination factors estimated
experimentally. When they used values from the literature, the estimated
source proportions differed considerably from the real values.
Ecologists interested in using mixing models are in a bind unless
they conduct experiments (Haramis et al. 2001). When discrimination factors from the literature are used, a sensitivity analysis that
examines the effect of variation in Δ is necessary. For the simplest
mixing models with two sources and one isotope, the value of a
source proportion depends only on two Δ values and the sensitivity
analysis can be done by applying the following equation:
fA =
δ T − (δB + ΔB )
.
(δ A + Δ A ) − (δB + ΔB )
eqn 5
The values of fA for the range of possible ΔA and ΔB values can be represented visually in a 3D plot with fA as the dependent variable. The
tools to do a sensitivity analysis for the 2-isotope, 3-source case have
not been developed. Even using Δ values measured experimentally is
not without problems. Discrimination factors are measured with
variation, and this variation will propagate when the mixing model
is solved. Current methods do not account for variation in discrimination factors. Because mixing models are used frequently, finding out
the effect of uncertainty in discrimination factors on the estimation
of source proportions is an area in which theoretical progress is
needed. In the future, studies that use discrimination factors in
mixing models should be accompanied by discussion about how
variation in their value, or errors in their estimation, contribute to
uncertainty in the calculation of source proportions.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 17–26
SIA in animal ecology 21
Routing
Mixing models assume that assimilated nutrients are disassembled into their elemental components and that these
elements are then reassembled into the molecules that make
up tissues. This assumption is unrealistic. For example, the
building blocks that animals use to manufacture tissues are
not carbon atoms, but the carbon skeletons of a myriad of
molecules. These carbon skeletons are conserved to various
degrees. For example, amino acids can be indispensable
and hence their carbon skeletons come from diet and are
indispensable. The carbon skeletons of the dispensable
amino acids come from either diet or are manufactured
endogenously from other macromolecules (Bequette 2003).
The differential allocation of isotopically distinct dietary
components to different tissues is called ‘isotopic routing’
(Schwarcz 1991).
Isotopic ecologists that work with omnivores that ingest
diets in which carbohydrates (and sometimes lipids) are
derived from one dietary source and protein is derived from
another can face a quandary. They may find that using different tissues for isotopic analyses to reconstruct an animal’s
diet might give different answers (Voigt et al. 2008). Worse,
using a single type of tissue might give the wrong answer
(Podlesak & McWilliams 2006). Although isotopic routing
was reviewed 15 years ago by Ambrose & Norr (1993), the
theme has received little attention from theoreticians and
experimenters.
Martínez del Rio & Wolf (2005) incorporated routing into
a mixing model for δ13C by assuming that the carbon in protein
was routed preferentially into tissue protein. Here we present
a simplified graphical version of this model (Fig. 2). We
assume that the animal ingests two dietary sources with
contrasting carbon isotopic compositions, one that only contains protein and another one that contains only carbohydrates.
We also assume that these diets provide other essential
macronutrients, but that the amounts of these materials
contribute little to the overall isotopic composition of diet.
This simple model predicts that the isotopic composition of
the animal’s tissue protein will be consistently higher than the
value expected from a mixing model. The predictions of the
model are consistent with the results of Ambrose & Norr
(1993) and Podlesak & McWilliams (2006).
Trophic discrimination factors
USES OF ISOTOPIC DISCRIMINATION
The observation that the isotopic composition of an animal’s
tissues differs from that of their diet has been useful. DeNiro
& Epstein (1981) noted that animal tissues were enriched in
15
N relative to their diets. This observation led to the conjecture
that the content of 15N in animal tissues is biomagnified along
the length of a food chain (Post 2002). This conjecture allows
ecologists to use δ15N to estimate an animal’s trophic level
(TL) using an equation devised by Vander Zanden et al. (1997)
and modified by Post (2002):
Fig. 2. A simple model of isotopic routing predicts that on diets in
which sources with contrasting δ13C are comprised primarily of
protein or carbohydrate, the δ13C of protein in tissues (solid curves)
will be intermediate between that predicted by a mixing model (solid
line) and that of the protein source (dashed parallel line). The model
also predicts that the isotopic composition of lipid will be
intermediate between that of a mixing line (dashed line, accounting
for the −3‰ fractionation resulting from the synthesis of lipid from
carbohydrate) and the δ13C of the carbohydrates source −3‰
(<39>Martinez del Rio & Wolf 2005). Closed points represent a diet
with high protein quality (i.e. with amino acid composition that
matches that of tissues), whereas open points represent a diet with
low protein quality.
15
TL = λ +
15
δ N c − δ N base
Δn
eqn 6
where, δ15Nc is the nitrogen isotopic composition of the
consumer, δ15Nbase is that of the food base, λ is the trophic level
of the base (λ = 1 if the base is primary producers), and Δn is
an estimate of the average increase in Δ15N per trophic level
(Post 2002). We argue that using the wrong estimated discrimination can lead to large errors in the estimation of fractional
source contributions. Eqn 6 relies on an estimated value of
Δ15N, and yet it has been applied successfully many times.
Vander Zanden & Rasmussen (2001) suggest that Δn is more
variable for herbivores (primary consumers) than for carnivores. Therefore, using primary consumers (i.e. λ = 2) as a
baseline reduces error in the estimation of TL. Vander
Zanden et al. (1997) found a relatively tight positive correlation between the average trophic positions of freshwater fish
estimated using δ15N and that estimated by gut content
analyses. Although eqn 6 is frequently used in terrestrial systems,
it has not been yet cross-validated.
15
N BIOACCUMULATION
Bioaccumulation of toxicants along a food chain occurs
because absorption is higher than elimination (Karasov &
Martinez del Rio 2007). If the same explanation applies to
15
N, Δ15N should have a positive value if animals retain 15N
preferentially over 14N (Martínez del Rio & Wolf 2005).
Available evidence supports this observation. The materials
excreted by the animals that have been measured tend to be
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 17–26
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N. Wolf et al.
isotopically lighter than tissues (reviewed by Tibbets et al.
2007). Sponheimer et al. (2003) questioned the 15N preferential
excretion hypothesis. They measured δ15N in the food and
excreta of llamas (Lama glama). They found that δ 15N
values of excreta were not more negative than that of food.
At steady state the isotopic composition of dietary inputs
should equal that of outputs (Martínez del Rio & Wolf 2005).
Therefore, finding that excreta are more depleted in 15N
than diet is a sufficient condition for a positive Δ15N value,
but it is not a necessary one. However, a positive Δ15N demands
that excreta are depleted in 15N relative to the animal’s body
– which was not measured by Sponheimer et al. (2003), and in
all cases measured this seems to be the case (Tsahar et al.
2007).
Olive et al. (2003) and Martínez del Rio & Wolf (2005)
constructed isotopic mass balance models to explain a positive
Δ15N. Martínez del Rio & Wolf’s (2005) model predicts that: (i)
Δ15N values should decrease with increased protein quality in
diet; (ii) Δ15N values should increase with diet’s protein content;
(iii) Δ15N values should decrease with the efficiency of nitrogen
deposition measured as the ratio between protein assimilation and protein loss; and (iv) Δ15N values should increase
with fasting time. Prediction (i) is supported by Robbins et al.
(2005) report of a highly significant interspecific negative
correlation between Δ15N values and diet’s protein value.
Prediction (ii) has mixed support: Pearson et al. (2003) found
a positive linear relationship between Δ15Nbody-diet values and
protein content in yellow-rumped warblers (Dendroica
coronata) and Focken (2001) found an increase in Δ15Nbody-diet
values with increased protein intake in Nile tilapia. In contrast,
Tsahar et al. (2007) found lower Δ15N values in fruit-eating
birds fed on diets with higher protein content, and Robbins
et al. (2005) found no effect of protein content in their comparative study. To our knowledge, prediction (iii) has not been
examined experimentally.
Martínez del Rio & Wolf’s (2005), model predicts that Δ15N
should increase with fasting time. This is a reasonable hypothesis
that has been posed repeatedly (see Gannes et al. 1997) but
that has received mixed support. Of eight studies on the effect
of fasting on invertebrates, five found a significant enrichment
in 15N and three found no effect. Because there are fewer
fasting studies in vertebrates, the patterns are less clear.
Hobson et al. (1993) found significant increases in δ15N values
in fasting geese that lost c. 50% of their body mass. In fasting
spawning salmon, only the liver became significantly enriched
in 15N in post-spawning kelts relative to pre-spawning adults
(Doucett et al. 1999). Castillo & Hatch (2007) fasted two
species of lizards (Anolis carolinensis and Uta stansburiana)
for 14 days and found that the tail muscles were not enriched
in 15N relative to those of fed animals. However, they found
that the δ15N values of excreta increased significantly from the
beginning to the end of the fast. McCue (2008) also found that
δ15N values in excreta increases along a 24-week fast in
rattlesnakes (Crotalus atrox), without a change in body δ15N
values.
In fasting animals not all organs lose nitrogen to the same
degree and in the same way (e.g. Doucett et al. 1999). Protein
is lost in a tissue because protein is broken down by proteases
into its component amino acids. The resulting amino acids
are then de-aminated in situ, or exported to other organs
(Caloin 2004). De- and trans-amination, should lead to
15
N-depleted nitrogenated by-products (ammonia, urea, and
uric acid) and a remaining pool of enriched amino acids that
can then be incorporated into proteins (Macko et al. 1986,
1987). Some organs such as muscle reduce their rate of
protein synthesis during a fast (Waterlow 2006). Therefore,
because these organs do not incorporate residual enriched
amino acids, we should not expect them to become enriched.
Other organs, such as liver retain high rates of protein
synthesis during a fast (Waterlow 2006). They manufacture
protein from the 15N-enriched pool of amino acids that
remains from protein catabolism. The organs that will
become 15N-enriched during a fast are those that maintain
significant synthesis.
DIFFERENCES IN TISSUE
Δ13 C
Tissue to diet discrimination factors differ among tissues
(McCutchan et al. 2003). The variation among tissues is
sometimes large. Reich et al. (2008) found that Δ13C varied
from 0·9‰ to 2·62‰ in the tissues of loggerhead turtles
(Caretta caretta). Other studies report differences of the same
magnitude in Δ13C values among tissues (McCutchan et al.
2003). Lipid content and amino acid composition are two
important candidates to explain inter-tissue differences in Δ13C
values. Lipid synthesis is accompanied by depletion in 13C
(DeNiro & Epstein 1977). Thus, some of the variation in
Δ13C values is explained by a tissue’s lipid content (Post et al.
2007). Lipids are not the only factor that can cause differences
in δ13C values among tissues. The δ13C values of amino acids
of primary producers can range widely. O’Brien et al. (2005)
reported differences of over 20‰ among the δ13C values of
indispensable amino acids in the foliage of several plant
species. They found that the δ13C values of the essential amino
acids in larval food plants was an excellent predictor of the
δ13C values of the essential amino acids in the eggs of nectarfeeding butterflies and moths. In contrast, because nonessentials were synthesized from carbon derived from adult
food, their δ13C value was more homogeneous and reflected
that of nectar sugars (see also Boggs 2009).
Howland et al. (2003) found that the carbon isotopic composition of individual dispensable amino acids in pig (Sus
scrofa) collagen was better predicted by the isotopic composition of bulk diet than by the composition of the individual
amino acids in diet. Howland et al. (2003) found a tight correlation between the δ13C value of dietary indispensable
amino acids and those in collagen for phenylalanine and
leucine. The δ13C value of other dietary indispensable amino
acids was a poor predictor of the δ13C value of those in collagen.
This is a disturbing result with no adequate explanation.
Howland et al. (2003) predicted accurately the isotopic composition of collagen from a mass balance model that includes
the amino acid composition of collagen and the δ13C value of
each individual amino acid in this tissue.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 17–26
SIA in animal ecology 23
Fig 3. The range in δ15N values within a
tissue (illustrated by horizontal bars at the
bottom of the graph) appears to increase
with trophic level. Some amino acids
(‘source’ amino acids, sensu McClelland &
Montoya 2002) appear to have consistently
low δ15N values, whether others appear to be
consistently 15N-enriched (‘trophic’ amino
acids). The rotifer Brachionus plicatillis and
the alga Tetraselmis suecica were cultured in
the laboratory by McClelland & Montoya
(2002). Popp et al. (2006) collected the data
for the muscle of wild caught yellow-fin tuna
(Thunnus albacares). Amino acids in bold
are indispensable.
DIFFERENCES IN TISSUE
15
Δ15 N
TL = 1 +
Tissues can differ greatly in δ15N value, and hence in
Δ15Ntissue-diet. δ15N values varied among tissues from −0·64‰ to
1·65‰ among the tissues of loggerhead turtles (Reich et al.
2007). The difference in δ15N values among tissues can be
explained by their amino acid content, and by the isotopic
composition of individual amino acids. δ15N values vary
among the amino acids of primary producers, and this variation
seems to be amplified by the physiological processes of
consumers (Fig. 6, McClelland & Montoya 2002; Popp et al.
2007). The δ15N value of amino acids in animal tissues seems to
have a bimodal distribution (Fig. 3). Some amino acids
appear to retain approximately the same nitrogen isotopic
composition of food, whereas others become enriched in 15N
by the animal’s metabolism. Popp et al. (2006) called the
relatively 15N-enriched amino acids ‘trophic’, and the relatively
15
N-depleted ones, ‘source’.
The heterogeneity in δ15N among amino acids within a
tissue not only allows explaining variation in nitrogen isotopic
composition among tissues, it also suggests that we might be
able to estimate an animal’s trophic position from information contained within the animal’s tissues. McClelland &
Montoya (2002) proposed using.
Δ15Nglutamate–phenylalanine = δ15Nglutamate − δ15Nphenylalanine
eqn 7
as an ‘internal’ index of trophic level. Recall that glutamate is
a trophic, whereas phenylalanine is a source amino acid.
McClelland & Montoya (2002) found that approximately
Δ15Nglutamate–phenylalanine = 7‰. Therefore, Popp et al. (2006) suggested using the following modifications of eqn 12:
15
d N trophic − d N source
7
eqn 8
where d15Ntrophic is the average δ15N of the trophic amino acids
and d15Nsource is the average δ15N of the source amino acids.
Popp et al. (2006) compared the TL estimates using these
equations with those obtained using eqn 6 and found roughly
comparable results. The assumption that 7‰ represents a valid
average increase in Δ15Nglutamate–phenylalanine or in Δ15Ntrophic–source
per trophic level in all systems must be tested. It seems risky
to derive a parameter that can be applied generally from a
single study.
Schmidt et al. (2004) measured the δ15N values in the
amino acids of Antarctic krill (Euphausia superba). They
found that females had more negative whole body bulk δ15N
values than males. They also found that within each sex, the
δ15N values of abdominal muscle were higher than that of
the digestive gland. These differences in δ15N values were the
result of differences in amino acid composition and in differences in isotopic composition among amino acids. They were
also the result of inter-sex differences in δ15N values between
the same amino acids. The trophic amino acids in females
tended to be more depleted in 15N than those in males, especially in the digestive gland. Source amino acids differed less
in δ15N values among tissues than trophic amino acids and
did not differ between males and females. Schmidt et al.
(2004) invoked similar physiological mechanism to those used
in a previous section (15N bioaccumulation) to explain differences in δ15N values between the same amino acid among different individuals. Briefly, they speculated that trophic amino
acids in individuals and tissues with high rates of transamination should be more enriched in 15N. The results of
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 17–26
24
N. Wolf et al.
Schmidt et al.’s (2004) study holds two cautionary lessons. (i)
If we apply eqn 8 to amino acids of the digestive gland, we
infer that males have a much higher trophic level than females.
If we apply it to data from the whole body, we infer that the
difference in trophic level between males and females is
much lower. The inferences that we make using TL equations
can be tissue-dependent. (ii) Schmidt et al. (2004) relied on
several lines of evidence to infer that male and female krill
feed on the same trophic level. Thus, they concluded that
differences in δ15N values between the trophic amino acids
of males and females were the result of differences in amino
acid metabolism. We need theory, more field observations,
and more experiments to find out how much δ15N values vary
among amino acids and to identify the factors that shape
this variation.
Routing, discrimination factors, and compound
specific isotopic analyses
Using isotopic analyses of bulk organic materials is an
undoubtedly invaluable tool for ecologists. However, we
have recognized that isotopic routing and the existence of
discrimination must be understood when interpreting results
of isotopic analyses of bulk materials. Compound specific
analyses are an invaluable tool that, together with data on the
composition of tissues can help interpret isotopic data.
Indeed, it seems possible to classify different compounds
depending on how sensitive they might be to routing and
trophic enrichment. Consider amino acids. It appears that we
can classify amino acids depending on the potential sources
of their carbon and nitrogen (Table 1). Animals lack the ability
to synthesize the amino acids that we consider indispensable,
and hence the carbon in these must be derived from dietary
sources without modification (reviewed by Karasov & Martinez
del Rio 2007). In the situation depicted in Fig. 2, we can
predict that the δ13C values in indispensable amino acids
reflect directly those of the corresponding amino acids in diet,
whereas the values of dispensable amino acids reflect a
mixture of all sources. The reasons why the N in some amino
acids is protected from isotopic enrichment are unknown, but
Table 1. Amino acids can be classified as dispensable or
indispensable depending on whether their carbon skeletons can be
manufactured by the animal or not. They can also be classified as
source or trophic, depending on whether their amino group is
relatively enriched in 15N presumably due to frequent transamination events
Indispensable
Dispensable
Source
Trophic
Phenylalanine
Threonine
Lysine
Serine
Glycine
Tyrosine
Isoleucine
Leucine
Valine
Aspartic acid
Glutamic acid
Proline
Alanine
we speculate that they include how freely each amino acid
exchanges nitrogen with others during trans-amination events.
Whether the amino acids are dispensable or indispensable is
not a potential criterion for 15N-enrichment (Fig. 3, Table 1).
It appears that the dispensable and N-promiscuous amino
acids involved in the transport and movement of nitrogen
(alanine and glutamic acid) tend to be 15N-enriched, whereas
those that are essential and not easily trans-aminated
(phenylalanine and threonine) tend to be relatively 15N-depleted.
The δ15N in some amino acids is perplexing. Proline and
serine receive their nitrogen from glutamate (a 15N-enriched
amino acid) during synthesis (Bequette, 2003). However,
proline is highly 15N-enriched, whereas serine is 15N-depleted
(Fig. 3). The mechanisms that lead to the source/trophic
dichotomy among amino acids are fertile arena for the
application of nutritional biochemistry to isotopic ecology.
Other isotopes
δ2H and δ18O values exhibit predictable patterns over the
earth’s surface waters, and they have received enormous
amounts of attention by researchers interested in finding the
site of origin of animals in a field that can be properly called
forensic ecology (reviewed by Rubenstein & Hobson 2004
and Bowen et al. 2005). However, few experimental studies
inform the inferences of the huge data sets already generated
by field observational research. For example, Doucett et al.
(2007) found large differences (c. 100‰) in δ2H values
between aquatic and terrestrial plants. Doucett et al. (2007)
also measured the δ2H values in aquatic insects and fish and
used mixing models to estimate the contribution of aquatic
and terrestrial sources to the diets of these animals. They
assumed that Δ2Htissues-diet = 0, and that the contribution of
hydrogen (H) body water to the hydrogen bound in the
organic compounds of tissues is negligible. These are two very
risky assumptions. We know little about whether there is
fractionation during the synthesis of biomolecules from
precursors and body water, and very little about the relative
contribution of hydrogen in body water and precursor dietary
nutrients to the hydrogen bound to biomolecules. In another
example, Birchall et al. (2005) reported large differences in
δ2H values between the collagen of carnivores and herbivores,
and assumed that these differences were the result of a
trophic/biomagnification effect. This effect is plausible, but
has so far, not been yet documented in a controlled feeding
study. Estimating this putative biomagnification effect
requires that diet and preformed water have the same δ2H
value. In short, the many potential applications of D and 18O
analyses in ecology demand that experimenters pay as much
attention to them as they have to C and N.
WE NEED MORE LABORATORY EXPERIMENTS AND
MORE THEORY
Perhaps not surprisingly, the number of observational field
studies that apply stable isotopes to ecological problems far
surpasses the number of experimental studies that aim to
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 17–26
SIA in animal ecology 25
clarify the mechanisms that explain the patterns that isotopic
ecologists find. Ten years ago, Gannes et al. (1997, 1998)
identified some of the areas that could be fruitfully explored
by experimentally minded isotopic ecologists. Although stable
isotopes have become firmly established as tools for animal
ecologists, many questions about their use still remain, and
most of these questions can only be resolved experimentally.
We hope that this review has identified how much progress
has been made in 10 years, but also how much remains to be
done. Hence, we end it with a renewed call for experimentation. Because experiments and observations are most efficient
at answering questions when informed by theory (National
Research Council 2007), we add to our call an exhortation for
the development of theoretical models.
Acknowledgements
The manuscript benefited from comments by Lenny Gannes, Kena Fox-Dobbs,
and Seth Newsome. Research on stable isotopes in CMRs laboratory has been
funded by NSF (IBN-0114016). This review is an updated and abbreviated
form of material presented in Martínez del Rio et al. (2008).
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Received 16 September 2008; accepted 19 November 2008
Handling Editor: David Raubenheimer
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Functional Ecology, 23, 17–26